The recent advancements in medical image segmentation have been marked by a significant shift towards more efficient, lightweight, and interactive solutions. Researchers are increasingly focusing on leveraging foundation models like the Segment Anything Model (SAM) to develop zero-shot segmentation capabilities, which require minimal human intervention and can significantly speed up the annotation process. Innovations such as the HoloLens-Object-Labeling (HOLa) application and the MCP-MedSAM model exemplify this trend, offering fully automatic annotation and lightweight adaptations that can be trained on limited resources. Additionally, there is a growing emphasis on reducing computational complexity and improving inference speed, as seen in the development of lightweight U-like networks utilizing neural memory Ordinary Differential Equations (nmODEs). These networks demonstrate substantial reductions in parameters and FLOPs while maintaining performance. Interactive and annotation-efficient frameworks are also gaining traction, with models like the Lightweight Interactive Network for 3D Medical Image Segmentation (LIM-Net) showcasing strong generalization capabilities and competitive accuracy with fewer interactions. Overall, the field is moving towards more automated, efficient, and user-friendly solutions that can be deployed in resource-constrained environments, significantly advancing the practical applications of medical image segmentation.