Semi-supervised learning (SSL) uses unlabeled data during training to learn
better models. Previous studies on SSL for medical image segmentation focused
mostly on improving model generalization to unseen data. In some applications,
however, our primary interest is not generalization but to obtain optimal
predictions on a specific unlabeled database that is fully available during
model development. Examples include population studies for extracting imaging
phenotypes. This work investigates an often overlooked aspect of SSL,
transduction. It focuses on the quality of predictions made on the unlabeled
data of interest when they are included for optimization during training,
rather than improving generalization. We focus on the self-training framework
and explore its potential for transduction. We analyze it through the lens of
Information Gain and reveal that learning benefits from the use of calibrated
or under-confident models. Our extensive experiments on a large MRI database
for multi-class segmentation of traumatic brain lesions shows promising results
when comparing transductive with inductive predictions. We believe this study
will inspire further research on transductive learning, a well-suited paradigm
for medical image analysis.