Estimating uncertainty in deep learning models is critical for reliable decision-making in high-stakes applications such as medical imaging. Prior research has established that the difference between an input sample and its reconstructed version produced by an auxiliary model can serve as a useful proxy for uncertainty. However, directly comparing reconstructions with the original input is degraded by information loss and sensitivity to superficial details, which limits its effectiveness. In this work, we propose Difference Reconstruction Uncertainty Estimation (DRUE), a method that mitigates this limitation by reconstructing inputs from two intermediate layers and measuring the discrepancy between their outputs as the uncertainty score. To evaluate uncertainty estimation in practice, we follow the widely used out-of-distribution (OOD) detection paradigm, where in-distribution (ID) training data are compared against datasets with increasing domain shift. Using glaucoma detection as the ID task, we demonstrate that DRUE consistently achieves superior AUC and AUPR across multiple OOD datasets, highlighting its robustness and reliability under distribution shift. This work provides a principled and effective framework for enhancing model reliability in uncertain environments.

翻译：在深度学习模型中估计不确定性对于医学成像等高风险应用中的可靠决策至关重要。先前研究已证实，输入样本与其由辅助模型生成的重构版本之间的差异可作为不确定性的有效代理。然而，直接比较重构结果与原始输入会因信息损失及对表面细节的敏感性而效果受限，从而制约了其有效性。本研究提出差分重构不确定性估计方法，该方法通过从两个中间层重构输入，并以二者输出之间的差异作为不确定性评分，从而缓解上述局限性。为在实践中评估不确定性估计，我们遵循广泛使用的分布外检测范式，将分布内训练数据与具有递增域偏移的数据集进行比较。以青光眼检测作为分布内任务，我们证明DRUE在多个OOD数据集上持续取得更优的AUC和AUPR，凸显了其在分布偏移下的鲁棒性与可靠性。本研究为增强模型在不确定环境中的可靠性提供了一个原理清晰且有效的框架。