This paper introduces the Constraint-Aware Neurosymbolic Uncertainty Framework (CANUF), a novel approach that unifies Bayesian deep learning with differentiable symbolic reasoning to provide trustworthy uncertainty estimates while respecting domain constraints. It significantly improves calibration and constraint satisfaction in scientific AI applications like materials science, molecular property prediction, and climate modeling.