Interactive segmentation plays a vital role in medical image analysis, facilitating accurate diagnosis and treatment planning through real-time interaction and rapid annotations. Scribble-based methods, where users draw over target structures, are particularly effective for delineating thin structures like vessels, providing precise pixel-level detail compared to bounding boxes. MedSAM, introduced in 2023, is optimized for bounding box inputs, which limits its effectiveness for precise interaction types such as scribbles. Additionally, it exhibits a slower inference due to its large size. To address these limitations, we evaluated simpler models such as thresholding, Meijering filters, and Geodesic Distance Transforms. These models outperformed MedSAM in segmentation accuracy and efficiency across fundus, microscopy, PET, and OCT, achieving a Dice Score of 62.31 and a Normalized Surface Dice of 67.01 on the validation set. Our findings highlight the effectiveness of traditional methods and reveal the current limitations of emerging foundation models. This comparative analysis aims to improve MedSAM’s robustness and efficiency, contributing to the development of a more reliable general model for medical image segmentation.