The performance of modern U-shaped neural networks for medical image segmentation has been significantly enhanced by incorporating Transformer layers. Although Transformer architectures are powerful at extracting global information, its ability to capture local information is limited due to their high complexity. To address this challenge, we proposed a new lightweight U-shaped cascade fusion network (LUCF-Net) for medical image segmentation. It utilized an asymmetrical structural design and incorporated both local and global modules to enhance its capacity for local and global modeling. Additionally, a multi-layer cascade fusion decoding network was designed to further bolster the network's information fusion capabilities. Validation performed on open-source CT, MRI, and dermatology datasets demonstrated that the proposed model outperformed other state-of-the-art methods in handling local-global information, achieving an improvement of 1.46% in Dice coefficient and 2.98 mm in Hausdorff distance on multi-organ segmentation. Furthermore, as a network that combines Convolutional Neural Network and Transformer architectures, it achieves competitive segmentation performance with only 6.93 million parameters and 6.6 gigabytes of floating point operations, without the need for pre-training. In summary, the proposed method demonstrated enhanced performance while retaining a simpler model design compared to other Transformer-based segmentation networks.