Cardiovascular diseases can be diagnosed with computer assistance when using the magnetic resonance imaging (MRI) image that is produced by the MRI sensor. Deep learning-based scribbling MRI image segmentation has demonstrated impressive results recently. However, the majority of current approaches possess an excessive number of model parameters and do not completely utilize scribbling annotations. To develop a feature decomposition distillation deep learning method, named FDDSeg, for scribble-supervised cardiac MRI image segmentation. Public ACDC and MSCMR cardiac MRI datasets were used to evaluate the segmentation performance of FDDSeg. FDDSeg adopts a scribble annotation reuse policy to help provide accurate boundaries, and the intermediate features are split class region and class-free region by using the pseudo labels to further improve feature learning. Effective distillation knowledge is then captured by feature decomposition. FDDSeg was compared with 7 state-of-the-art methods, MAAG, ShapePU, CycleMix, Dual-Branch, ZscribbleSeg, Perturbation Dual-Branch as well as ScribbleVC on both ACDC and MSCMR datasets. FDDSeg is shown to perform the best in DSC(89.05% and 88.75%), JC(80.30% and 79.78%) as well as HD95(5.76% and 4.44%) metrics with only 2.01 M of parameters. FDDSeg methods can segment cardiac MRI images more precise with only scribble annotations at lower computation cost, which may help increase the efficiency of quantitative analysis of cardiac.