Well, at the end it depends on the task you want to perform. As example to train a SOAT deep neural network on this dataset http://www.vision.caltech.edu/visipedia/CUB-200.html 10 the difference in using an ImageNet pre-trained model and training from scratch is up to a 20% in accuracy or greater.
This means that not only for the FCN, but for any challenging task using pre-trained models on good and general datasets, such as ImageNet, improve performance.  This happens because the model has already learned to extract general features for a general task (image-classification, object-detection, language-classification…) and you finally train your model with a fine-tunning. It is something similar as what Hinton did with the stacked restricted boltzmann machines models.