Bi-encoder architectures for biomedical entity linking learn flat embedding spaces that ignore ontology structure. HiCo-EL encodes hierarchical knowledge into bi-encoder representations through hierarchical contrastive pretraining. The core idea is within-space hierarchical multi-label contrastive learning (HMLC): contrastive losses applied independently within each encoder space, preserving cross-space compatibility for retrieval. Our formulation drops the monotonicity constraints of prior HMLC work, handles variable-depth hierarchy paths, and adds depth-weighted alignment between encoder spaces. Treating each entity as a leaf node unifies coarse type structure with fine-grained entity discrimination. On MedMentions and NCBI Disease, HiCo-EL outperforms ArboEL by +3.7% and +1.8% Recall@1 respectively. The gains reveal a key insight: breadth amplifies, depth generalizes—broad hierarchies help common entities most (+6.3% R@1), while deep hierarchies benefit unseen entities (+9.6% R@1 zero-shot).