  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.
