Recent studies introduced deep models to synthesize clinical contrast-weighted images from magnetic resonance fingerprinting (MRF). While these studies reported high synthesis quality, they require supervision from fully-sampled training data of clinical contrasts that might be challenging to collect due to scan time considerations. To avoid reliance on full supervision, we propose a semi-supervised model (ssMRF) that can be trained directly using accelerated references. To achieve this, ssMRF introduces a semi-supervised loss function based only on acquired k-space samples of target contrasts. ssMRF further leverages complementary Poisson disc masks in a multi-task learning framework for synergistic synthesis of multiple contrasts. Retrospective experiments demonstrate the efficacy of ssMRF where the method yields high-quality synthesis performance across different clinical contrasts on par with the fully-supervised alternative.