The field of machine learning is moving towards incorporating uncertainty awareness in various applications, including injury prediction, disease diagnosis, and remaining useful life prediction. Researchers are exploring innovative approaches to quantify and calibrate uncertainty, such as deep ensemble-based uncertainty quantification, Bayesian output layers, and probabilistic modeling. These methods have shown significant improvements in predictive performance and reliability, enabling risk-aware decision-making in critical domains. Notably, papers such as 'Enhancing Multi-Label Thoracic Disease Diagnosis with Deep Ensemble-Based Uncertainty Quantification' and 'Uncertainty-Aware Deep Learning Framework for Remaining Useful Life Prediction in Turbofan Engines' demonstrate the potential of uncertainty-aware machine learning in transforming clinical decision support systems and safety-critical predictions. Other notable papers include 'Clinician-in-the-Loop Smart Home System to Detect Urinary Tract Infection Flare-Ups via Uncertainty-Aware Decision Support' and 'I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation', which highlight the importance of uncertainty quantification in real-world applications.