Despite extensive research on neural network calibration, existing methods typically apply global transformations that treat all predictions uniformly, overlooking the heterogeneous reliability of individual predictions. Furthermore, the relationship between improved calibration and effective uncertainty-aware decision-making remains largely unexplored. This paper presents a post-hoc calibration framework that leverages prediction reliability assessment to jointly enhance calibration quality and uncertainty-aware decision-making. The framework employs proximity-based conformal prediction to stratify calibration samples into putatively correct and putatively incorrect groups based on semantic similarity in feature space. A dual calibration strategy is then applied: standard isotonic regression calibrated confidence in putatively correct predictions, while underconfidence-regularized isotonic regression reduces confidence toward uniform distributions for putatively incorrect predictions, facilitating their identification for further investigations. A comprehensive evaluation is conducted using calibration metrics, uncertainty-aware performance measures, and empirical conformal coverage. Experiments on CIFAR-10 and CIFAR-100 with BiT and CoAtNet backbones show that the proposed method achieves lower confidently incorrect predictions, and competitive Expected Calibration Error compared with isotonic and focal-loss baselines. This work bridges calibration and uncertainty quantification through instance-level adaptivity, offering a practical post-hoc solution that requires no model retraining while improving both probability alignment and uncertainty-aware decision-making.

翻译：尽管神经网络校准研究已相当深入，现有方法通常采用全局变换统一处理所有预测，忽视了单个预测的异质可靠性。此外，改进的校准与有效的基于不确定性的决策之间的关系在很大程度上仍未得到探索。本文提出一种后验校准框架，利用预测可靠性评估共同提升校准质量与基于不确定性的决策能力。该框架采用基于邻近性的共形预测方法，根据特征空间中的语义相似性将校准样本分层为推定正确组与推定错误组。随后实施双重校准策略：对推定正确预测采用标准等渗回归校准置信度，而对推定错误预测则采用欠置信正则化等渗回归将其置信度向均匀分布方向降低，从而便于识别这些预测以进行进一步检验。我们通过校准指标、不确定性感知性能度量及经验共形覆盖率进行了综合评估。在CIFAR-10和CIFAR-100数据集上使用BiT与CoAtNet骨干网络的实验表明，与等渗回归及焦点损失基线方法相比，所提方法实现了更少的自信错误预测，并获得了具有竞争力的预期校准误差。这项工作通过实例级自适应性搭建了校准与不确定性量化之间的桥梁，提供了一种无需模型重训练即可同时改进概率对齐与不确定性感知决策的实用后验解决方案。