Medical image segmentation has become a cornerstone for many healthcare applications, allowing for the automated extraction of critical information from images such as Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRIs), and X-rays. The introduction of U-Net in 2015 has significantly advanced segmentation capabilities, especially for small datasets commonly found in medical imaging. Since then, various modifications to the original U-Net architecture have been proposed to enhance segmentation accuracy and tackle challenges like class imbalance, data scarcity, and multi-modal image processing. This paper provides a detailed review and comparison of several U-Net-based architectures, focusing on their effectiveness in medical image segmentation tasks. We evaluate performance metrics such as Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) across different U-Net variants including HmsU-Net, CrossU-Net, mResU-Net, and others. Our results indicate that architectural enhancements such as transformers, attention mechanisms, and residual connections improve segmentation performance across diverse medical imaging applications, including tumor detection, organ segmentation, and lesion identification. The study also identifies current challenges in the field, including data variability, limited dataset sizes, and issues with class imbalance. Based on these findings, the paper suggests potential future directions for improving the robustness and clinical applicability of U-Net-based models in medical image segmentation.