Creating and maintaining biomedical terminologies for multiple natural languages is a resource-expensive task, typically carried out by human domain experts. We here report on efforts to computationally support this process by treating term acquisition as a machine translation-guided classification problem capitalizing on parallel corpora. We report on experiments for French, German, Spanish and Dutch parts of a UMLS-derived terminology for which we generated 18k, 23k, 19k and 12k new terms and synonyms, respectively. Based on expert assessments of a novel German terminology segment about 80% of the newly acquired terms were judged as bio-medically reasonable and terminologically valid.