The field of machine learning is moving towards more efficient labeling and active learning techniques, with a focus on reducing the need for large amounts of labeled data. Recent developments have shown that selective querying of the most informative samples can significantly improve model performance, while minimizing labeling costs. This is particularly important in applications such as medical imaging, where labeling can be time-consuming and costly. Noteworthy papers in this area include Is Complete Labeling Necessary, which proposes a novel active learning framework for longitudinal medical imaging, and Hierarchical Semi-Supervised Active Learning for Remote Sensing, which integrates semi-supervised learning and active learning for remote sensing applications. Additionally, papers such as StereoDETR and MetaDCSeg have demonstrated the effectiveness of efficient labeling and active learning techniques in 3D object detection and medical image segmentation, respectively. Overall, the field is moving towards more efficient and effective labeling techniques, with a focus on reducing costs and improving model performance.