Medical image segmentation plays a crucial role in clinical applications such as computer-aided diagnosis and surgical navigation， aiming to extract different organs and lesions from complex medical images accurately. However， the existing U-shaped network architecture suffers from the problems such as high information redundancy in skip connections and high computational complexity. To address these challenges， a lightweight medical image segmentation network named ES-TransUNet （Efficient channel attention and Simple-TransUNet） was proposed. In the network， the Criss-Cross Attention （CCA） mechanism was introduced in the encoder to capture long-range dependencies and the multi-head attention structure in Transformer was optimized， so as to lighten the model. Dynamic upsampling （Dysample） module was introduced in the decoder to improve upsampling efficiency. At the same time， in order to reduce the information redundancy in skip connections， the Simple COntextual Transformer （SCOT） block was introduced to filter out redundant features. Experimental results on the Synapse multi-organ segmentation and ACDC datasets demonstrate that ES-TransUNet achieves 2.37 and 1.57 percentage points improvements， respectively， in Dice Similarity Coefficient （DSC） compared to TransUNet； and reduces the Hausdorff Distance （HD） by 9.69 approximately on the Synapse dataset. Additionally， the results of comparing proposed network with state-of-the-art medical segmentation models indicate that ES-TransUNet maintains high segmentation accuracy while reducing model parameters and computational complexity significantly， and improves inference efficiency. It can be seen that ES-TransUNet is more satisfied the practical requirements in real-time medical image segmentation.