Automatic and standardized methods for 4D transesophageal echocardiography (TEE) annotation and mitral valve (MV) motion and morphology analysis are difficult to achieve due to the known limitations of echocardiography and the limited availability of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo-labelling to achieve MV segmentation in 4D TEE. The proposed strategy is based on a Teacher-Student framework that guarantees the generation of pseudo-labels with high reliability. 120 4D TEE recordings from 60 candidates to MV repair are used. The Teacher model, an ensemble of three convolutional neural networks (CNNs), is trained on end-systole (ES) and end-diastole (ES) frames and is used to generate MV pseudo segmentations on intermediate frames of the cardiac cycle. The pseudo annotated frames augment the training set of the Student model, enhancing its segmentation accuracy and temporal consistency. The Student model achieves superior performance than individual Teacher models with Dice Score of 0.82, average surface distance of 0.37 mm and 95% Hausdorff distance of 1.72 mm for the MV leaflets, and lower coordinate predictions error for the mitral annulus. The Student model demonstrates reliable frame-by-frame MV segmentation, accurately identifying leaflet morphology and dynamics throughout the cardiac cycle, with a significant reduction in inference time compared to the Ensemble model. This approach significantly reduces the manual annotation workload and ensures reliable, repeatable, and time-efficient MV analysis. Our method holds strong potential for improving the accuracy and efficiency of MV diagnostics and treatment planning in clinical settings.