In the past decade, the advances in deep learning technologies have enabled their application to medical image segmentation, showing great potential. Nonetheless, the scarcity of available labelled data can result in a lack of model generalisability. This is especially true for supervised methods requiring annotated data. Data augmentation can be used to partially alleviate data scarcity when training deep learning models. In particular, the use of deep learning-based generative modelling, which allows for the sampling of synthetic data from the modelled data distribution, has shown its potential for data augmentation in the past years. In this work, we address the topic of generative modelling to generate images and annotations, going over brainSPADE, a 2D and 3D generative model of healthy and pathological segmentations and corresponding multi-modal images for brain MRI, and how the synthetic data it produces can be applied to a range of segmentation tasks to mitigate the effects of data scarcity or domain shift.