The need for artificial intelligence (AI)-driven computer-assist ed diagnosis (CAD) tools drives up the demand for large high-quality datasets in medical imaging. However, collecting the necessary amount of data is often impractical due to patient privacy concerns or restricted time for medical annotation. Recent advances in generative models in medical imaging with a focus on diffusion-based techniques could provide realistic-looking synthetic samples as a supplement for real data. In this work, we study whether synthetic volumetric MRIs generated by the diffusion model can be used to train downstream models, e.g., semantic segmentation. We can create an arbitrarily large dataset with ground truth by conditioning the diffusion model with a segmentation mask. Thus, the additional synthetic data can be used to control the dataset diversity. Experiments revealed that downstream tasks profit from additional synthetic data. However, the effect will eventually diminish when sufficient real samples are available. We showcase the strength of the synthetic data and provide practical recommendations for using the generated data in zonal prostate segmentation.