The core of knowledge distillation lies in transferring the teacher’s rich ’dark knowledge’—subtle probabilistic patterns that reveal how classes are related and the distribution of uncertainties. While this idea is well established, teachers trained with conventional cross-entropy often fail to preserve such signals. Their distributions appear decisive but are in fact brittle, offering little beyond the hard label. This overconfidence is especially problematic in high-cardinality tasks, where subtle distinctions among many plausible classes are crucial for guiding compact students. To address this limitation, we revisit distillation from a distributional perspective and propose Calibrated Uncertainty Distillation (CUD), a framework that makes dark knowledge more faithfully accessible. Instead of uncritically inheriting the teacher’s overconfidence, CUD encourages the teacher to expose informative uncertainty and guides the student to learn from calibrated targets instead of overly sharpened predictions. By shaping the teacher’s predictive distribution before transfer, our approach balances accuracy and calibration, allowing students to benefit from both confident signals on easy cases and structured uncertainty on hard ones.