Active learning has the potential to reduce labeling costs in medical image segmentation by selecting only the most informative samples. However, conventional approaches typically rely on model-based informativeness measures, limiting the expert’s role to passively annotating pre-selected images. This restricts expert-driven prioritization of segmentation targets aligned with clinical objectives. To address this limitation, we propose GALMIS (Guided Active Learning for Medical Image Segmentation), a novel framework that integrates expert-driven guidance into the informative sample selection process. By leveraging submodular subset selection, GALMIS ensures that selected samples are not only informative but also clinically relevant to predefined segmentation targets. We evaluate our approach in both simulated and real active learning scenarios on: (1) foreground-foreground class imbalance in abdominal CT, and (2) clinical targets for coronary artery segmentation in cardiac CT. Our results demonstrate improved labeling efficiency on clinically relevant targets compared to conventional active learning methods. Code is available at https://github.com/Berni1557/TAL .