Some initialization (albeit very limited) is required by the user (which may impede its effectiveness)
You can usually automate seed placement and thus have a completely automatic method. For example you can register a generic “atlas” image and transferring seeds to the patient image. Or you may use simple global thresholding to get candidate regions and keep the most likely candidate (that meets minimum size and shape requirements). You may also use the segmentation that sct provides as seed: you shrink it by a few mm and use that as spinal cord segment, and expand by a centimeter and use that as background segment. The advantage is that you can add more seeds manually to fix segmentation in regions where sct registration failed.
Grow from seeds and Watershed allows interactive seed editing, so they work on any images, they can even separate regions where there is no image contrast at all. The question is how much additional seed is needed. If you spend several minutes specifying additional seeds then you may just as well use more manual methods, such as interpolate between slices.
“Interpolate between slices” effect allows you to interpolate segmentation between slices. For example, you can segment on 20-30 slices and the effect computes segmentation between these. You can then review segmentation results and if you find that deviation on any interpolated slice is too much then you segment that single slice and the full segmentation is updated immediately.
We can give you more specific advice, if you provide example images and information on constraints (what should be segmented exactly, how accurate the segmentation has to be, how much time is available for segmentation, etc).