Accurate vestibule segmentation for CT images is of great significance for the clinical diagnosis of congenital ear malformations and cochlear implant. However, it is still a challenging task due to extremely small size and irregular shape of vestibule. Here, a vestibule segmentation network for CT images is proposed under the basic encoder-decoder framework. Firstly, a residual block based on channel attention mechanism, named Res-CA block, is designed to guide the network to enhance the important features for the segmentation tasks while suppressing the irrelevant ones. And then, a global context-aware pyramid feature extraction (GCPFE) module is proposed to capture multi-receptive-field global context information. Finally, active contour with elastic (ACE) loss function is adopted to guide network learning more detailed information of the boundary. Furthermore, deep supervision (DS) mechanism is employed to locate the boundaries finely, improving the robustness of the network. The experiments are conducted on the self-established VestibuleDataset and UHRCT-Dataset, as well as publicly available retinal dataset, namely DRIVE, to comprehensively verify the robustness and generalization capability of the proposed segmentation network. The experimental results show that the proposed network can achieve a superior performance.