Since deep learning has been applied to medical image analysis, convolutional neural networks (CNNs) and Vision Transformers (ViTs) have become core architectures for medical image classification tasks. However, CNNs are constrained by local receptive fields and cannot effectively model long-range context-dependent correlations in medical images; ViTs face deployment challenges in high-resolution medical image tasks due to the quadratic computational complexity of self-attention. While State Space Models (SSMs) represented by Mamba offer a new solution with linear complexity, they suffer from redundant directional modeling and low parameter efficiency in direct medical image applications. This study proposes the efficient state space enhancement framework (MedMamba-ESS), integrating the SS2D-Top2 adaptive directional scanning mechanism (reducing SSM submodule FLOPs by ~37%) and the G-SSM grouped parameter sharing module (achieving 3–4% parameter compression and 4.4% accuracy improvement via regularization). Validated on 14 public datasets (9 imaging modalities, 9 anatomical regions, >240,000 images), MedMamba-ESS achieves superior performance at 2.0G FLOPs: its Overall Accuracy (OA) is 3% higher than the baseline MedMamba-Tiny on non-MedMNIST datasets (3.13% higher than other mainstream models) and 3% higher on MedMNIST datasets (1% higher than others). Ablation experiments confirm that the two modules synergistically reduce parameters by 1.05% and boost accuracy by 4.6%. This study overcomes the technical limitations of traditional SSMs in medical imaging applications, achieving synergistic improvements in both model performance and computational efficiency. It provides an architecture optimization solution that combines practicality and generalizability for the implementation of SSMs in medical image analysis.