Supervised deep learning techniques have demonstrated remarkable performance in segmentation tasks in various domains including medical, biomaterial, and bioengineering fields. The advancement in imaging technologies has enabled the gathering of large amounts of raw data. Nevertheless, acquiring fully annotated ground-truth datasets for supervised deep learning-based segmentation tasks has remained a challenge attributed to the availability of expert resources and the laborious task of pixel-level annotation. Self-supervised learning(SSL) techniques have gained the momentum to learn representations from unlabeled datasets while also preserving the domain-specific information over transfer learning techniques thereby overcoming the challenge of annotating huge datasets. Weakly supervised learning(WSL) techniques have gained attention to address the annotation challenge by using weak annotations such as scribbles, dot annotations, and bounding boxes instead of pixel-wise annotations. In this work, we leveraged the advantages of both SSL and WSL to perform segmentation of single cells in optical images of biofilms attached on single-layer graphene-coated copper substrates guided by scribble annotations. Conditional random fields are applied as a post-processing to further improve segmentation performance. The method showed promising performance in segmenting single cells on biofilm images with scribble annotations that make up only 40-50% of pixel-level annotations.