Uncertainty Score

Uncertainty scores quantify the confidence of a model's predictions, aiming to improve the reliability and trustworthiness of machine learning systems across diverse applications. Current research focuses on developing and evaluating methods for estimating uncertainty in various tasks, including regression, classification, and segmentation, often employing techniques like Monte Carlo dropout and deep ensembles within architectures such as U-Nets. This work is crucial for enhancing decision-making in safety-critical domains like medical diagnosis and autonomous driving, where understanding model limitations is paramount, and for improving the overall robustness and reliability of AI systems. A key challenge remains establishing standardized evaluation protocols to ensure meaningful comparisons and guide future research.