Segmentation of the placenta from fetal MRI is critical for planning of fetal surgical procedures. Unfortunately, it is made difficult by poor image quality due to sparse acquisition, inter-slice motion, and the widely varying position and orientation of the placenta between pregnant women. We propose a minimally interactive online learning-based method named Slic-Seg to obtain accurate placenta segmentations from MRI. An online random forest is first trained on data coming from scribbles provided by the user in one single selected start slice. This then forms the basis for a slice-by-slice framework that segments subsequent slices before incorporating them into the training set on the fly. The proposed method was compared with its offline counterpart that is with no retraining, and with two other widely used interactive methods. Experiments show that our method 1) has a high performance in the start slice even in cases where sparse scribbles provided by the user lead to poor results with the competitive approaches, 2) has a robust segmentation in subsequent slices, and 3) results in less variability between users.