In spectral 3D CT data, the traditional convolution has a poor ability to capture global features, and the full-scale self-attention mechanism consumes large resources. To solve this problem, this study introduces a new visual attention paradigm, the wave self-attention (WSA). Compared with the ViT technology, this mechanism uses fewer resources to obtain the same amount of self-attention information. In addition, to more adequately extract the relative dependency among organs and to improve the robustness and execution speed of the model, a plug-and-play module, the wave random-encoder (WRE), is designed for the WSA mechanism. The encoder is capable of generating a pair of mutually inverse asymmetric global (local) position information matrices. The global position matrix is used to globally conduct random sampling of the wave features, and the local position matrix is used to complement the local relative dependency lost due to random sampling. In this study, experiments are performed on the task of segmenting the kidney and lung parenchyma in the standard datasets Synapse and COVID-19. The results show that this method outperforms existing models such as nnFormer and Swin-UNETR in terms of accuracy, the number of parameters, and inference rate, arriving at the SOTA level.