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：深度神经网络中预测不确定性的精确估计是机器学习和统计建模中实现可靠决策的关键要求，尤其在医疗AI场景中。共形预测（CP）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而备受关注。然而，在共形预测中量化模型不确定性仍是一个活跃的研究领域，尚未得到完全解决。本文探索了最先进的CP方法及其理论基础，提出了一种概率方法，用于量化共形预测中生成预测集所蕴含的模型不确定性，并为计算得出的不确定性提供了认证边界。通过这种方式，我们使得CP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，例如MC-Dropout和DeepEnsemble，以及证据方法）进行对比。