In current medical image segmentation tasks, the combined transformer and convolutional architectures excel in capturing global cues and local details, but still pose two main concerns from a layer-level perspective: (1) intra-layer issue: the existing methods inefficiently obtain and fuse global-local information, potentially resulting in incomplete feature extraction; (2) inter-layer issue: the most of methods follow the classical U-shape structure, which inevitably leads to information weakening in the encoder-decoder. In light of these, we propose InjectionNet from the perspective of layers, mainly comprising the Intra-layer Global-Local Injection (GLI) module and Inter-layer Weight Injection (WI) modules. GLI employs multi-scale convolution for local information extraction and flexibly uses a multi-head self-attention mechanism for efficiently capturing global information and fusing them effectively. WI enhances information transfer by injecting generated feature weights, with different variants to suit various network stages. Extensive experiments on three medical imaging public datasets demonstrate the superior performance of InjectionNet compared to previous works.