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Uncertainty quantification in machine learning (ML) interatomic potentials remains fundamentally limited by the conflation of epistemic uncertainty, arising from incomplete sampling of configuration space, and aleatoric uncertainty, embedded in reference data generated by density-functional theory. Existing approaches provide internally consistent uncertainty estimates but collapse these distinct sources into a single scalar, obscuring the mechanisms governing model reliability and limiting principled decision-making. This work introduces a modular, architecture-agnostic framework that enforces explicit separation of epistemic and aleatoric contributions at the level of model design rather than post hoc analysis. The framework defines five interoperable components—a shared feature extractor, dedicated epistemic and aleatoric modules, an aggregation mechanism, and a calibration stage—whose interactions preserve disentanglement throughout training, inference, and downstream application. The resulting formulation transforms uncertainty into an operational diagnostic. Epistemic uncertainty identifies regions where additional data acquisition is informative, whereas aleatoric uncertainty defines the intrinsic accuracy ceiling imposed by the reference method. This separation restructures active learning by directing sampling toward reducible error, enables meaningful comparison between models through their uncertainty composition, and grounds performance evaluation relative to an explicit noise floor. The framework further introduces operational criteria that provide falsifiable tests of successful separation, ensuring that reported uncertainties remain interpretable and consistent across architectures. By decoupling learnable structure from irreducible variability, the proposed approach establishes a principled foundation for uncertainty-aware ML potentials, supporting more efficient data allocation, more reliable atomistic simulations, and more rigorous standards for model development in computational materials science.