Uncertainty quantification of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result. In recent years, a numerous number of uncertainty quantification methods have been introduced. Monte Carlo dropout (MC-Dropout) is one of the most well-known techniques to quantify uncertainty in deep learning methods. In this study, we propose two new loss functions by combining cross entropy with Expected Calibration Error (ECE) and Predictive Entropy (PE). The obtained results clearly show that the new proposed loss functions lead to having a calibrated MC-Dropout method. Our results confirmed the great impact of the new hybrid loss functions for minimising the overlap between the distributions of uncertainty estimates for correct and incorrect predictions without sacrificing the model's overall performance.

翻译：机器学习与深度学习方法的不确定性量化在增强对结果可信度方面发挥着重要作用。近年来，已有大量不确定性量化方法被提出。蒙特卡洛丢弃法（MC-Dropout）是深度学习方法中最广为人知的不确定性量化技术之一。本研究通过将交叉熵与期望校准误差（ECE）和预测熵（PE）相结合，提出两种新型损失函数。实验结果表明，新型损失函数能够有效实现校准后的MC-Dropout方法。研究结果证实，新型混合损失函数能在不牺牲模型整体性能的前提下，显著减少正确预测与错误预测之间不确定性估计分布的重叠程度。