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Uncertainty quantification (UQ) has become a routine component of materials artificial intelligence (AI), with predictive models now systematically reporting confidence intervals or variance estimates alongside outputs spanning formation energies to mechanical properties. Despite this integration, the designation “trustworthy uncertainty” remains conceptually unresolved. Assertions of reliability are frequently decoupled from operational criteria that connect statistical behavior to the concrete decisions faced by materials scientists, including large-scale screening, experimental prioritization, design optimization, certification under regulatory constraints, and the interpretation of anomalous phenomena. Addressing this gap, the present boundary-focused analysis advances a definition of trustworthiness grounded in decision relevance and introduces the construct of decision-ready confidence. This formulation identifies a set of boundary conditions that collectively determine whether uncertainty estimates can support action. Calibration ensures correspondence between predicted uncertainty and empirical error distributions, while sharpness constrains interval width to maintain discriminative value without sacrificing validity. A related requirement concerns the separation of epistemic and aleatoric components, enabling differentiation between reducible and irreducible uncertainty. Coverage, particularly in its conditional form, establishes reliability at the level of individual predictions, and stability enforces robustness under small perturbations of input space. These statistical conditions are complemented by computational tractability, which situates uncertainty estimation within the temporal constraints of decision-making processes. Crucially, none of these properties is intrinsic in isolation; each must be interpreted relative to the decision context in which the model is deployed. To anchor these criteria, the analysis delineates a set of recurring decision regimes that structure materials AI workflows, spanning screening, experimental validation, active learning, optimization, certification, and discovery. Each regime imposes distinct requirements on uncertainty behavior, thereby redefining trustworthiness as a context-dependent alignment rather than a universal attribute. Building on this premise, a framework for decision-ready confidence is introduced to formalize the mapping between decision type, required UQ properties, validation procedures, risk thresholds, and reporting practices. This framework integrates and extends established approaches, including epistemic–aleatoric decomposition, conformal prediction, and broader trustworthy machine-learning paradigms, while situating them within the operational realities of materials engineering. In doing so, it establishes a coherent conceptual foundation for evaluating and deploying UQ methods whose outputs are not only statistically sound but also directly actionable in advancing materials discovery and ensuring system-level reliability.