Multiple reads may be assigned to single unique (transcript) molecules. Specifically, if several reads share the same cell barcodes, map to the same gene, and share the same polyN sequence (with allowance for sequence errors), they are assigned to the same transcript.
With counts of unique molecules and reads, sequencing saturation is calculated as:
1 - (# of unique molecules detected) / (# of total reads)
So for example, if you detected 800,000 unique molecules with 1M reads you would get:
1 - 800,000/1,000,000 = 0.2 saturation
Conceptually, one can think of saturation like this, going from lower to higher sequencing depth. Initially, at low sequencing depth, there is a high probability that each read represents a different molecule. In this case, the ratio of unique molecules to reads is close to 1, so 1 - 1 yields saturation close to zero. Later on, with greater depth, more and more of the reads will be redundant (e.g. duplicated during PCR) so the denominator grows faster than the numerator. This gives a smaller molecule to read ratio, which means 1 - small yields saturation closer to 1.
If the sequencing saturation in your experiment is low, this means that additional sequencing will yield many more detected transcripts and genes per cell. As your sequencing saturation gets high (>0.5), you are approaching the point of diminishing returns and additional sequencing will be less productive in terms of detecting additional transcripts and genes per cell.