Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decision-making in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP) has emerged as a promising framework for representing the model uncertainty by providing well-calibrated confidence levels for individual predictions. However, the quantification of model uncertainty in conformal prediction remains an active research area, yet to be fully addressed. In this paper, we explore state-of-the-art CP methodologies and their theoretical foundations. We propose a probabilistic approach in quantifying the model uncertainty derived from the produced prediction sets in conformal prediction and provide certified boundaries for the computed uncertainty. By doing so, we allow model uncertainty measured by CP to be compared by other uncertainty quantification methods such as Bayesian (e.g., MC-Dropout and DeepEnsemble) and Evidential approaches.

翻译：精准估计深度神经网络中的预测不确定性是机器学习和统计建模中可靠决策的关键要求，尤其在医学人工智能领域。共形预测作为一种通过为个体预测提供校准良好的置信水平来表示模型不确定性的框架，已展现出广阔前景。然而，共形预测中模型不确定性的量化仍是一个活跃的研究领域，尚未得到充分解决。本文探讨了先进的共形预测方法及其理论基础，提出了一种基于概率的方法来量化共形预测中由生成预测集推导出的模型不确定性，并为计算出的不确定性提供了认证边界。通过这一方法，我们使得共形预测度量的模型不确定性能够与其他不确定性量化方法（如贝叶斯方法中的MC-Dropout和DeepEnsemble，以及证据方法）进行比较。