Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and clinical-grade data. To enhance their performance, we employed a semi-supervised diffusion model, obtaining a 0.90(p<0.01) correlation between chronological and predicted age on clinical-grade T1w MR images. This outperformed standard non-generative methods. Furthermore, the prediction's produced by our model were significantly associated with survival duration (r=0.24, p<0.01) in Amyotrophic Lateral Sclerosis. Thus, our approach demonstrates the value of diffusion-based architectures for the task of brain age prediction.