Accurate segmentation of anatomical structures and abnormalities in medical images is crucial, but manual segmentation is time-consuming and automated approaches lack clinical accuracy. In recent years, active learning approaches that aim to combine automatic segmentation with manual input have gained attention in the field, aiming to reduce the annotation effort required for training segmentation models. Batch query retrieval is a key component of active learning as it is a technique that allows for the simultaneous selection of multiple regions/points for annotation. This study investigates the effectiveness of discrete batch query retrieval methods compared to the traditional approach using gradient descent in the context of 3D medical image segmentation. Our experiments show that the active learning paradigm with batch query retrieval provides a few advantages over the gradient descent approach. Additionally, we analyze the impact of discretizing the query retrieval strategy on system performance. Our findings suggest that discretization can lead to slight performance degradation in terms of segmentation quality but offer computational advantages and faster convergence. We also discuss open issues, such as the interpretability of active learning methods, and recommend further research on combining active learning with other segmentation techniques. Overall, our study contributes to the understanding of active learning in medical image segmentation and provides insights for developing more efficient and accurate interactive segmentation models.